MCQs

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Overall MCQs for Reference

Which of the following is a goal of clustering algorithms?
a. Classification
b. Regression
c. Dimensionality reduction
d. Grouping similar data points together
Answer: d. Grouping similar data points togetheraimcqs
Which clustering algorithm is based on the concept of centroids?
a. K-Means
b. DBSCAN
c. Agglomerative
d. Mean-Shift
Answer: a. K-Meansaimcqs
Which clustering algorithm does not require specifying the number of clusters in advance?
a. K-Means
b. DBSCAN
c. Agglomerative
d. Mean-Shift
Answer: b. DBSCANaimcqs
Which clustering algorithm is sensitive to the order of the data points?
a. K-Means
b. DBSCAN
c. Agglomerative
d. Mean-Shift
Answer: d. Mean-Shiftaimcqs
Which clustering algorithm is based on a density-based approach?
a. K-Means
b. DBSCAN
c. Agglomerative
d. Mean-Shift
Answer: b. DBSCANaimcqs
Which clustering algorithm uses a hierarchical approach to create clusters?
a. K-Means
b. DBSCAN
c. Agglomerative
d. Mean-Shift
Answer: c. Agglomerativeaimcqs
Which clustering algorithm is based on the concept of medoids?
a. K-Means
b. DBSCAN
c. Agglomerative
d. K-Medoids
Answer: d. K-Medoidsaimcqs
Which clustering algorithm is capable of detecting outliers as noise points?
a. K-Means
b. DBSCAN
c. Agglomerative
d. Mean-Shift
Answer: b. DBSCANaimcqs
Which clustering algorithm is suitable for non-linearly separable data?
a. K-Means
b. DBSCAN
c. Agglomerative
d. Mean-Shift
Answer: d. Mean-Shiftaimcqs
Which clustering algorithm assigns data points to the nearest cluster centroid?
a. K-Means
b. DBSCAN
c. Agglomerative
d. Mean-Shift
Answer: a. K-Meansaimcqs
Which clustering algorithm is computationally efficient for large datasets?
a. K-Means
b. DBSCAN
c. Agglomerative
d. Mean-Shift
Answer: b. DBSCANaimcqs
Which clustering algorithm can handle clusters of varying shapes and sizes?
a. K-Means
b. DBSCAN
c. Agglomerative
d. Mean-Shift
Answer: b. DBSCANaimcqs
Which clustering algorithm does not require the assumption of equal-sized clusters?
a. K-Means
b. DBSCAN
c. Agglomerative
d. Mean-Shift
Answer: d. Mean-Shiftaimcqs
Which clustering algorithm is based on the concept of nearest neighbors?
a. K-Means
b. DBSCAN
c. Agglomerative
d. K-Nearest Neighbors
Answer: d. K-Nearest Neighborsaimcqs
Which clustering algorithm is sensitive to the initial placement of cluster centroids?
a. K-Means
b. DBSCAN
c. Agglomerative
d. Mean-Shift
Answer: a. K-Meansaimcqs
Which clustering algorithm is based on the concept of minimizing the within-cluster variance?
a. K-Means
b. DBSCAN
c. Agglomerative
d. Mean-Shift
Answer: a. K-Meansaimcqs
Which clustering algorithm is suitable for high-dimensional data?
a. K-Means
b. DBSCAN
c. Agglomerative
d. Mean-Shift
Answer: a. K-Meansaimcqs
Which clustering algorithm is capable of identifying dense regions in the data?
a. K-Means
b. DBSCAN
c. Agglomerative
d. Mean-Shift
Answer: b. DBSCANaimcqs
Which clustering algorithm is based on the concept of similarity or distance between data points?
a. K-Means
b. DBSCAN
c. Agglomerative
d. Mean-Shift
Answer: a. K-Meansaimcqs
Which clustering algorithm can handle categorical data?
a. K-Means
b. DBSCAN
c. Agglomerative
d. K-Modes
Answer: d. K-Modesaimcqs
Which clustering algorithm is based on the concept of density-reachable points?
a. K-Means
b. DBSCAN
c. Agglomerative
d. Mean-Shift
Answer: b. DBSCANaimcqs
Which clustering algorithm is capable of handling noise and outliers effectively?
a. K-Means
b. DBSCAN
c. Agglomerative
d. Mean-Shift
Answer: b. DBSCANaimcqs
Which clustering algorithm is suitable for time-series data?
a. K-Means
b. DBSCAN
c. Agglomerative
d. K-Shape
Answer: d. K-Shapeaimcqs
Which clustering algorithm is based on the concept of merging the closest clusters iteratively?
a. K-Means
b. DBSCAN
c. Agglomerative
d. Mean-Shift
Answer: c. Agglomerativeaimcqs
Which clustering algorithm is suitable for large datasets with an unknown number of clusters?
a. K-Means
b. DBSCAN
c. Agglomerative
d. Mean-Shift
Answer: d. Mean-Shiftaimcqs
Which clustering algorithm is based on the concept of fuzzy sets and membership degrees?
a. K-Means
b. DBSCAN
c. Fuzzy C-Means
d. Mean-Shift
Answer: c. Fuzzy C-Meansaimcqs
Which clustering algorithm can be used to identify overlapping clusters in the data?
a. K-Means
b. DBSCAN
c. Agglomerative
d. Density-Based Overlapping Clustering
Answer: d. Density-Based Overlapping Clusteringaimcqs
Which clustering algorithm is based on the concept of minimizing the sum of squared errors within each cluster?
a. K-Means
b. DBSCAN
c. Agglomerative
d. Mean-Shift
Answer: a. K-Meansaimcqs
Which clustering algorithm does not directly utilize a distance metric?
a. K-Means
b. DBSCAN
c. Agglomerative
d. Graph-Based Clustering
Answer: d. Graph-Based Clusteringitfeature
What is the main objective of the K-Means clustering algorithm?
a. Minimize the Euclidean distance between points
b. Maximize the number of clusters
c. Minimize the distance between cluster centroids
d. Maximize the number of data points in each cluster
Answer: c. Minimize the distance between cluster centroidsgeeksforgeeks
Which metric does K-Means clustering use to measure distance between points?
a. Manhattan Distance
b. Euclidean Distance
c. Cosine Similarity
d. Hamming Distance
Answer: b. Euclidean Distancegeeksforgeeks
What does the Elbow Method help determine in K-Means clustering?
a. Optimal number of clusters
b. Cluster centroids
c. Feature scaling
d. Outliers
Answer: a. Optimal number of clustersgeeksforgeeks
Which type of learning is K-Means clustering categorized under?
a. Supervised Learning
b. Unsupervised Learning
c. Reinforcement Learning
d. Semi-Supervised Learning
Answer: b. Unsupervised Learninggeeksforgeeks
How are initial cluster centroids chosen in K-Means clustering?
a. Randomly
b. Based on highest values
c. Based on lowest values
d. Manually assigned
Answer: a. Randomlygeeksforgeeks
What is the disadvantage of the K-Means clustering algorithm?
a. Always provides the best clustering
b. Handles categorical data efficiently
c. Sensitive to initial centroid selection
d. Automatically determines the number of clusters
Answer: c. Sensitive to initial centroid selectiongeeksforgeeks
Which parameter is optimized in the Elbow Method?
a. Centroid position
b. Number of clusters (K)
c. Number of iterations
d. Learning rate
Answer: b. Number of clusters (K)geeksforgeeks
Which of the following is used to cluster text documents in K-Means?
a. Term Frequency-Inverse Document Frequency (TF-IDF)
b. Euclidean Distance
c. Decision Trees
d. Gradient Descent
Answer: a. Term Frequency-Inverse Document Frequency (TF-IDF)geeksforgeeks
What is WCSS in K-Means clustering?
a. Weighted Cluster Sum of Squares
b. Within-Cluster Sum of Squares
c. Whole Cluster Sample Score
d. Weighted Cluster Sample Score
Answer: b. Within-Cluster Sum of Squaresgeeksforgeeks
What is the stopping condition for K-Means clustering?
a. When all points are classified
b. When the centroids no longer change
c. When maximum iterations are reached
d. Both b and c
Answer: d. Both b and cgeeksforgeeks
Which clustering algorithm can handle both numeric and categorical features?
a. K-Means
b. K-Modes
c. DBSCAN
d. Fuzzy C-Means
Answer: b. K-Modesgyansetu+1
Which of the following is a partitional clustering technique?
a. Hierarchical
b. K-Means
c. DBSCAN
d. All of the above
Answer: b. K-Meanssanfoundry
In clustering, ‘noise’ refers to:
a. Well-defined clusters
b. Outlier data points
c. Cluster centroids
d. Data duplication
Answer: b. Outlier data pointsaimcqs
Which linkage method is used in hierarchical clustering to compute the distance between clusters?
a. Complete
b. Single
c. Average
d. All of the above
Answer: d. All of the abovesanfoundry
Which type of clustering creates a dendrogram?
a. K-Means
b. Hierarchical
c. DBSCAN
d. K-Medoids
Answer: b. Hierarchicalsanfoundry
Which algorithm would you use for discovering clusters of arbitrary shape?
a. K-Means
b. DBSCAN
c. Agglomerative
d. K-Medoids
Answer: b. DBSCANaimcqs
In k-means++, the initial centroids are chosen:
a. Randomly
b. To maximize minimum squared distance
c. According to data order
d. Using all points as centroids
Answer: b. To maximize minimum squared distanceitfeature
What should be minimized in k-means clustering for best results?
a. Cluster count
b. Within-cluster sum of squares (WCSS)
c. Feature dimensions
d. Outlier presence
Answer: b. Within-cluster sum of squares (WCSS)geeksforgeeks
Which characteristic is NOT a strength of DBSCAN?
a. Finds clusters of arbitrary shape
b. Handles outliers
c. Requires specifying k in advance
d. Detects noise
Answer: c. Requires specifying k in advanceaimcqs
Which measure is often used to evaluate clustering results?
a. Accuracy
b. Silhouette score
c. Precision
d. Recall
Answer: b. Silhouette scoreitfeature
Which clustering algorithm is best for high-dimensional, sparse data, such as text?
a. K-Means
b. K-Medoids
c. DBSCAN
d. Spectral Clustering
Answer: a. K-Meansitfeature+1

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